Confidence Intervals for Adaptive Trial Designs II: Case Study and Practical Guidance

Thomas Burnett, David S. Robertson, Babak Choodari-Oskooei, Munya Dimairo, Michael Grayling, Philip Pallmann, Thomas Jaki

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Abstract

In adaptive clinical trials, the conventional confidence interval (CI) for a treatment effect is prone to undesirable properties such as undercoverage and potential inconsistency with the final hypothesis testing decision. Accordingly, as is stated in recent regulatory guidance on adaptive designs, there is the need for caution in the interpretation of CIs constructed during and after an adaptive clinical trial. However, it may be unclear which of the available CIs in the literature are preferable. This paper is the second in a two-part series that explores CIs for adaptive trials. Part I provided a methodological review of approaches to construct CIs for adaptive designs. In this paper (Part II), we present an extended case study based around a two-stage group sequential trial, including a comprehensive simulation study of the proposed CIs for this setting. This facilitates an expanded description of considerations around what makes for an effective CI procedure following an adaptive trial. We show that the CIs can have notably different properties. Finally, we propose a set of guidelines for researchers around the choice of CIs and the reporting of CIs following an adaptive design.
Original languageEnglish
Article numbere70202
JournalStatistics in Medicine
Volume44
Issue number18-19
Early online date8 Aug 2025
DOIs
Publication statusPublished - 8 Aug 2025

Bibliographical note

For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) license to any Author Accepted Manuscript version arising.

Data Availability Statement

R code to reproduce all the findings of this study is available at https://github.com/dsrobertson/CIs_for_ADs.

Funding

This work was supported by the UK Medical Research Council (MC_UU_00002/14, MC_UU_0040_03, and MC_UU_00004_09), the Health and Care Research Wales, the Cancer Research UK, and the National Institute for Health and Care Research (NIHR).

Keywords

  • adaptive design
  • bootstrap
  • conditional inference
  • coverage
  • estimation
  • group sequential
  • interim analyses

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